Multi-level feature re-weighted fusion for the semantic segmentation of crops and weeds

Abstract

Intelligent farm robots empowered by proper vision algorithms are the new agricultural machinery that eases weed control with speed and accuracy. Based on the farmland substantial similarity between the crops, and weeds, or other background interference objects, an improved deep convolutional neural network (DCNN) algorithms is proposed for the pixel semantic segmentation of crop and weed. First, a lightweight backbone is proposed to balance the features map textual and shape signals, which are essential cues for better crop and weed prediction. Second, a multi-level feature re-weighted fusion (MFRWF) module is suggested to combine only the relevant information from every backbone layer output to improve the contextual maps of crops and weeds. Finally, a decoder is designed based on convolutional weighted fusion (CWF) to preserve the relevant crop and weed context information by reducing the possible feature context distortion. Experimental results show that our improved neural network obtained the mean intersection of union (MIOU) scores of 0.8646, 0.9164, and 0.8459 on the carrot/weed field image (CWFID), sugar beet (BoniRob), and Rice seedling datasets, respectively. Therefore, the results have not only outperformed the commonly used architectures but can precisely identify crops/weeds and substantially improve the robot inference speed with minimal memory overhead. The code is available at:https://github.com/jannehlamin/MFRWF.

Keywords

• Deep learning
• Pixel-wise classification
• Semantic segmentation
• Weeds detection
• Precision farming
• Robotic vision